Abstract *
This paper uses year-to-year variation in temperature to estimate the long-term
effects of climate change on health outcomes in Mexico. Combining temperature
data at the district level and three rounds of nationally representative household
surveys, an individualâ&#x20AC;&#x2122;s health as an adult is matched with the history of heat
waves from birth to adulthood. A flexible econometric model is used to identify
critical health periods with respect to temperature. It is shown that exposure to
higher temperatures early in life has negative consequences on adult height. Most
importantly, the effects are concentrated at the times where children experience
growth spurts: infancy and adolescence. The robustness of these findings is
confirmed when using health outcomes derived from accidents, which are
uncorrelated with early exposure to high temperatures.
JEL classifications: I12, Q54, Q41
Keywords: Global warming, Climate change, Health, Mexico

*

This paper was prepared as part of the Inter-American Development Bank Research Department project â&#x20AC;&#x153;The
Health Impacts of Climate Change in Latin America and the Caribbean.â&#x20AC;?

1. Introduction
The frequency of heat waves is increasing rapidly in Latin America and the Caribbean. In
Mexico City, for example, the number of heat spells (i.e., days with temperatures over 30ºC or
86ºF) between 1991 and 2000 is almost twice the number recorded in 1970s and three times
more than in the 1950s (Jáuregui, 2009). Compared to the 1870s, heat waves are now nine times
more common. Furthermore, as shown in Figure 1 below, one important aspect of climate
change in Mexico is the expected shift to the right in the distribution of temperatures. By 2070,
heat waves are expected to become even more frequent than today.
What is the expected impact of this aspect of climate change on human health? This is a
relatively new area of research in economics. Most studies have concentrated on the effects of
climate change, measured by extreme temperatures including heat waves, in high-income
countries, ignoring developing countries altogether, including those in Latin America and the
Caribbean. 1 Furthermore, the few studies that focus on heat waves in richer nations tend to
estimate the contemporaneous effects on mortality rates. While mortality is an important
outcome, the vast majority of the population survives heat waves. Thus, a natural question to ask
is whether heat waves have a long-lasting effect. Does exposure to a heat wave as a child affect
the person’s adult health? If so, is the effect more pronounced when exposure occurs at a
younger age?
This paper answers these questions in the context of Mexico. I use random year-to-year
variation in temperature to estimate the long-term effects of exposure to high temperatures on
health outcomes in Mexico. A key advantage is the use of temperature data at the district level
(municipalidad in Mexico) combined with three nationally representative cross-sectional
household surveys. These data allow me to match an individual’s health as an adult with her
history of exposure to heat waves in each stage of her life cycle until adulthood. The combined
cross-sectional surveys provide a sample of over 65,000 individuals born between 1960 and
1990, a period where the frequency of heat waves was increasing rapidly.
This paper contributes to the existing literature in several ways. First, I use temperature
data within states, which permits a more precise estimation of the effect of climate change. Most
papers in the United States use data at the state level, limiting the inference derived from these
papers. Second, I estimate long-term effects by examining whether exposure to extreme
1

temperatures early in life affects adult health. Using height as health outcome, I show that
exposure to high temperatures has a negative impact on adult health. Third, the econometric
model permits the comparison of exposure early in life vis-à-vis later periods. The results
suggest that infancy (between 1 and 4 years of age) and adolescence (between 10 and 15) are the
most critical periods. These periods coincide with the growth spurts of humans.
Furthermore, I conduct two falsification tests. When weight is used as a measure of adult
health, my results show no effect from extreme temperatures. This is expected because, unlike
height, weight is a measure of short-term health (see Strauss and Thomas, 1995, for a detailed
review). Thus, the possible negative effects of heat waves on weight could be remediated by
future investments. However, lacking the necessary health status to grow at critical periods
cannot be reversed by future investments. I use the likelihood of experiencing an accident in the
last 12 months prior to the surveys as a placebo test. The validity of the identification strategy is
reinforced, as I find no effect of heat waves on this outcome.
The paper explores heterogeneous effects by gender and poverty level of the district. The
results indicated no gender differences in the impact of heat waves. However, the effects are
more negative for individuals growing up in poorer districts. The results of this investigation
provide academics and policymakers with the most comprehensive analysis to date of the longterm effects of climate change on health outcomes in the region.

2. Brief Review of the Literature
The literature on the effects of climate change on health outcomes is small but growing. As
reviewed by Deschênes (2012), the bulk of papers has focused on developed countries (however,
see Burgess et al., 2011, regarding India); have mainly studied the effect of the temperature; and
evaluate the contemporaneous impact. To keep this section brief and to focus the attention of this
document on the contributions of my work I will discuss papers closely related to mine. For a
more extensive review please see Deschênes (2012) and the papers therein.
The work by Deschênes and Greenstone (2011) is the closest to my paper. They explore
the effect of extreme temperatures, both hot and cold, on the age-specific mortality rates in the
U.S. during the past few decades. Their identification strategy exploits the random nature of
temperature changes across states to estimate the effect on mortality during that same year, after
accounting for state and year fixed effects as well as state trends. They find, for example, that an
3

extra day with a mean temperature exceeding 90°F (the measure for heat waves in the United
States), relative to a day in the 50°–60°F range, is associated with an increase in the annual ageadjusted mortality rate of about 0.11 percent. However, the effects are not linear because “a day
with a mean temperature below 20° F is associated with an increase in annual mortality of
roughly 0.07–0.08 percent” (p. 153). The authors find that effects are the largest for infants and
the elderly. A similar strategy is used in Deschênes, Greenstone and Guryan (2009), where
extreme temperatures are linked to lower birth weights, also in the United States, and by
Deschênes and Moretti (2009) for the case of migration. Guerrero Compeán (2013) applies this
methodology to estimate the contemporaneous effect on mortality in Mexico and finds
ambiguous results.
In all of these papers, the data force the authors to aggregate their measure of temperature
at the state level. 2 This is mainly due to the lack of mortality data at the county level. Thus, we
could expect the precision of their findings to be somewhat comprised due to data limitations.
That is not the case in my paper. Like the previous papers, I use data at the station level.
However, unlike those papers, the outcome of interest is available at the individual level and I
can identify the district where the person resides. 3 Thus, I can measure extreme temperatures (of
heat only for Mexico) at a finer and more relevant level: the district.
Second, as shown above, extreme temperatures are associated with higher mortality rates
but the effects are very small (or ambiguous.) Thus, the vast majority of people survive a heat
wave, including children. However, are the effects limited to mortality? The main contribution of
my paper is to show the long-lasting effects of heat waves. Given my findings, concentrating
only on mortality will underestimate the effect of heat waves on health. Thus, I take advantage of
the richness of my data and ask whether a person’s adult health could be linked to exposure to
heat waves while growing up. The exploration of the long-term effects addresses the limitation
that Deschênes (2012) has identified in the existing literature. Most importantly, using a model
borrowed from the literature on skill formation, I test which are the most vulnerable stages of a
person’s life cycle until adulthood in regards to health. This model is described below.

2
3

Similar to my paper, Guerrero Compeán (2013) uses data at the district level.
Below I discuss why between-state mobility is not a limiting factor for my analysis of Mexico.

4

3. Conceptual Framework
To identify the long-term effect of exposure to extreme temperatures across different stages of
the life cycle on health outcomes I adapt the model of “skill formation” introduced by Cunha et
al. (2006). 4 A key issue in this model is the departure from the previous literature where
“childhood” was treated as a single period (e.g., Becker and Tomes, 1986) Instead, Cunha et al.
(2006) consider a model where inputs at different stages of child development could have a
differential impact and these inputs could be seen either as complements or substitutes. To adapt
this model in terms of estimating the effect of climate change, consider the health status of a
person at time t as ht. In its simplest form, ht is a scalar, but the model can be easily extended to
the case where ht is a vector. The goal of the model is to describe how health evolves over time
and what is the (possibly) differential role of exposure to disinvestments such as extreme weather
in early (e.g., childhood) vis-à-vis later periods (e.g., adolescence). 5
Assume that the technology of production of health when a person is t years old is given
by
ht+1=ft(x, ht,Wt)

t=1,2,…,T

(1)

where ht+1 and ht are the stocks of health at time t+1 and t, respectively, for a child who was born
with health endowment h0, while parents’ characteristics are captured in x (and are assumed to be
time invariant). Children are exposed to weather shocks Wt in each period. Thus, a recursive
representation of equation (1) leads to
ht +1=mt(x, h0,W1,…,Wt)

t=1,2,…,T

(2)

Equation (2) implies that future health status depends on the initial health endowment h1,
parental characteristics and the full history of weather shocks since t=1. It is straightforward to
see how this equation helps us study how weather shocks affect adult health status. First, we set
T=21 years of age, as most humans in the Western world, for example, reach their adult height at
that age (see Deaton, 2007; Case and Paxson, 2009; and Agüero and Deolalikar, 2013, for
references). Second, because data on birth weight are not available for adults in the household
4

Almond and Currie (2011) use this model, for example, to consider the case of the formation of human capital
before age five.
5
The full model could consider overlapping generations (parents and their children) each living for T periods with
common preferences and inelastic labor supply and discusses the optimal timing of investments. Adding these
features to this simplified version does not affect the main conclusions of this model.

5

survey described below, I assume that initial health status, h0, is a function of the person’s
gender, birth cohort and location. Third, the parental characteristics (e.g., mainly education and
access to durable goods) are approximated using Census data matched by the birth cohort.
Finally, the sequence of weather shocks W1,W2,…,WT is captured by the temperature recorded in
each period.
This model will allow me to evaluate how adult health (at T+1) depends on weather
shock at time t, that is, Wt for t≤T. Formally, this is given by
= θt for t=1,2,…,T.

(3)

Furthermore, the model permits the comparison of exposure to hotter days in different
stages of the life cycle. For example, we can compare the effect on early childhood (θearly) vis-àvis adolescence (θadolescence). This will identify the sensitive periods regarding adult health. The
next section discusses the data to be used in the estimations.

4. Data Sources
There are three main data sources for this paper. The data on health status come from three
rounds of the Encuesta Nacional de Salud y Nutrición (ENSANUT), Mexico’s nationally
representative health and nutrition survey. The three cross-sectional surveys were conducted in
2000, 2006 and 2012, and they cover all health aspects of a randomly selected sample of
households. Relevant to this paper is the inclusion of questions such as height and weight. In
order to conduct placebo tests, I include an outcome that is unlikely to be related to heat waves:
accidents. Having access to these three outcomes is an important advantage of the ENSANUT.
The average sample size of adults between the ages of 21 and 50 is close to 65,000 for all three
surveys. 6
In addition to this rich set of health outcomes, the ENSANUT includes information about
the person’s age and location. I use this information to construct the sequence of heat waves. The
second main data source comes from Mexico’s National Weather Service, which collects daily
information from all the meteorological stations across the nation. Figure 2 shows the location of
each of the more than five thousand weather stations across Mexico. For each station and day,

6

See http://ensanut.insp.mx/ for more details about the three surveys.

6

the maximum and minimum temperatures are collected together with information about
precipitation levels. I match the household data with the weather information using the
coordinates of each station and the location of the district where the individual lives from the
ENSANUT. It is important to note that between-state migration is unlikely to be a source of bias
for this paper. The Mexican census of the last 20 years shows that over 90 percent of individuals
live in the same district (and state) as in the previous iteration, reducing the downward bias that
migration could introduce into the estimates. 7
The third main source of data comes from Mexico’s National Population Council
(CONAPO). We use CONAPO’s district-level poverty index to explore heterogeneous effects. 8
The poverty index is a function of measures of education (percent of population that are illiterate
or without primary education), housing (percent of houses without water, sewage, electricity,
non-dirt floors), and access to goods (having a refrigerator) in a given locality. These three main
datasets will be used to estimate the effects of changes in temperature as described in equations
(2) and (3) above.

where hijscy refers to adult health status (i.e., height) of person i, from district j, located in state s,
born in cohort c and observed in survey year y. Equation (4) includes fixed effects at the district
(αj), birth cohort (αc) and survey year (αy) as well as state trends (αsc). These controls account
for the possible unobserved factors that remain constant over time and those which, nationwide,
vary year of birth of the cohort, by survey year and by state over time. Vector zijscy includes
controls to approximate the initial health endowment (h0, in the model described in Section 3) as
well as parental characteristics (x). These variables include gender, marital status, and education
of the individual.
7

Migrants out of Mexico are not included in the household survey. While this does not affect the internal validity of
the paper it could reduce its external validity. Exploring this issue goes beyond the scope of this paper and should be
addressed in future work.
8
The index can be downloaded for free from
http://www.conapo.gob.mx/es/CONAPO/Indices_de_Marginacion_2010_por_entidad_federativa_y_municipio.

7

Function g(t) captures the association between adult health and temperatures at different
stages of the life cycle. Note that g(t) varies by birth cohort and district, but to focus on the
functional form I suppress these indexes in the discussion below. I follow Deschênes and
Greenstone (2011) and consider a flexible form that divides the temperature at stage t in 10ºF
bins. In this case g(t) is characterized by
g(t)=ΣbθbtTEMPtb

for t=1,…,T

(5)

The variables TEMPtb denote the number of days in stage t where the daily mean
temperature is in the b-th of the 10 bins between 50ºF to 90ºF. A key functional form assumption
is that the impact of the daily mean temperature on adult health status is constant within 10° F
intervals.
Consistent with the current literatue, the validity of this paper’s empirical strategy is
based on the assumption that the estimation of equation (4) will produce unbiased estimates of
the θbt vector for all t. Vector θ is identified from district- and cohort-specific deviations in
weather (i.e., the fixed-effects discussed above) after controlling for shocks common to all
districts in a state. Furthermore, due to the random year-to-year variation in weather, it seems
reasonable to believe that this variation is orthogonal to unobserved determinants of health
status. Therefore, for the case of the b-th temperature variable, the identifying assumption that
E[gjc(t) eijscy | gjc(t), λzijscyc, αj, αc, αy, αsc] = 0 is very likely to be valid. In this regard, the
identification assumptions made in this work are analogous to the papers by Deschênes and
Greenstone (2011) and Deschênes, Greenstone and Guryan (2009).
As shown in Figure 3, there is enough within-year variation to estimate the effects. For
example, I plot the residuals from regressing the percentage of days in a year where the
temperature is above 80˚F in each weather station against station fixed effects. The figure shows
that for each year, the percentage of hot days is between 20 and 83 percent and that half of the
data exhibits a variation larger than 10 percentage points.
Also, it is likely that the error terms are correlated within district or within district-bycohort groups over time. To address this issue, I ran all regressions with standard errors that
allow for heteroskedasticity of an unspecified form and that are clustered at the district level and
a second specification clustering at the district-by-cohort group level. I found no difference
between these approaches, so in this paper I present the former; however, the results using the

8

latter method of clustering are available upon request. The estimates of equations (4) and (5) are
shown in the next section.

6. Does Exposure to Hotter Temperatures Have Long-Lasting Effects?
6.1 Main Results
Table 1 shows trends in hot temperatures in Mexico. There, I regress the percentage of hot days
against a linear trend and station fixed-effects. In Panel A, I limit the sample to years from 1960
to 2011. In column (1), an additional year is associated with an increase in the proportion of days
above 80˚F of 0.0012. This means that in 20 years, the proportion of those hot days increases by
3.95 percent (=0.0012*20/.608). The corresponding number for days above 85˚F is higher. In 20
years Mexico has experienced a 4.44 percent increase of these hot days (column 2). For days
above 90˚F, the number is even bigger: 7.52 percent over two decades (column 3). Note that this
increase is observed after controlling for the time-invariant characteristics of the districts.
Furthermore, as shown in Panel B, in the years since 1980, the corresponding increments are
larger. In 20 years, the percentage of days with temperatures 90˚F or more has increased by 9.6
percent. What is the effect of these higher temperatures on those exposed while growing up?
To answer this question I estimate equation (4) and the results are shown in Appendix
Table A.1. To simplify the number of parameters in vector θ, I aggregated the stages of the life
cycle into four groups: in utero (exposure in the year before birth), infancy (aged between 1 and
4), childhood (5-9) and adolescence (10-15.) Also, the temperature variables capture the
proportion of days above T˚F, where T={50, 60, 70, 80, 85 and 90}. To ease their interpretation,
I calculate the effect on adult outcomes of having more hot days relative to 50˚F, after
controlling for fixed effects at the district, birth cohort and survey year as well as state trends and
individual characteristics (i.e., education, marital status and gender.) For example, let θ50 and θ80
be, respectively, the parameters capturing the effect on height of having days in the 50s˚F and
days in the 80s˚F when the person was an infant and reported in Table A.1 (column 1). Thus, I
plot the effect relative to the impact of temperature at 50˚F, that is θ80-θ50 (solid lines), as well as
its 95 percent confidence interval (dashed lines). These estimates are shown in Figures 4-6
below.
In Figure 4, I use height as the adult health outcome. The figure shows its association
with temperature for the four stages of the life cycle described above. The results indicate that
9

exposure to hotter temperatures relative to mild ones (50˚F) is negatively associated with height
as an adult. This negative association is also found in the other growth-spurt period: adolescence.
Not surprisingly, I found no effect for the periods where human growth is slower.
I repeat this analysis but now focusing on weight as measure of health. Unlike height,
weight measures short-term health and it should be less affected by temperature while growing
up. This is precisely what it is shown in Figure 5. Exposure to colder or hotter temperatures
rather than 50˚F is not associated with the weight, and this is true for all stages of the life cycle.
In Appendix Figure A.1 I considered an alternative measure of health: self-reported overall good
health. This variable is equal to one if the individual feels that she is “satisfied” or “very
satisfied” with her overall health status. However, a main concern with this variable, beyond the
fact that it is self-reported and lacks the accuracy of anthropometric variables such as height and
weight, is that a person could internalize a chronic health condition and be less likely to report it
as a problem. In this case, a true negative impact of heat waves would not be observed. This is
precisely what is observed in Figure A.1, where exposure to higher temperatures does not change
the individual’s perception of her health.
6.2 Robustness Checks and Heterogeneous Effects
The findings suggest that exposure to hotter temperatures (relative to 50˚F) has a negative
association if it happens at stages of the life cycle where human growth is faster. That is,
individuals exposed to these weather shocks have not been able to (completely) buffer these
negative effects. The lack of an effect for short-term outcomes such as health serves as a placebo
test. In Figure 6, I take this test even further and focus on an outcome that is not linked to
temperature: the likelihood of suffering an accident in the last 12 months prior to the household
survey. As shown in that figure, there is no link between the likelihood of accidents and exposure
to different temperatures while growing, and this reinforces the validity of the identification
strategy.
I consider now whether the effects of higher temperatures have differential impacts by
gender and by the poverty status of the district. In both cases I altered equation (4) to include an
interaction term with all the temperature variables. The results for the case of gender are shown
in Figure 7. There is no significant difference in the impact of weather on height by gender.
However, as shown in Figure 8, we can observe that the effect is more negative for individuals
10

living in areas with higher levels of marginalization (poorer areas) when they experience higher
temperatures during adolescence. These differential effects are not present for other stages of the
life cycle.
6.3 Mechanisms
The findings presented above provide robust evidence of the long-term effect of heat waves.
Exposure to heat waves not only affects health outcomes by the contemporaneous effect on
mortality but has long-lasting consequences as shown on adult height. In this subsection I discuss
some possible mechanisms leading to this long-lasting impact. First, exposure early in life to
higher temperatures can affect adult health via the impacts on pollution. For example, Bharadwaj
and Eberhard (2010), in the case of Chile, show that changes in temperature negatively affect
birth outcomes through augmenting the level air pollution.
Second, Epstein et al. (1980), Ramsey (1995), Hancock, Ross, and Szalma (2007),
Pilcher, Nadler, and Busch (2002) show significant negative effects of high temperature on
cognitive performance. This is important, as Case and Paxson (2009) demonstrate that cognitive
performance and nutrition share the same inputs. A third possible mechanism could arise from
changes in agricultural productivity and the availability of food. This is discussed, for example,
by Guerrero Compeรกn (2013) and by macroeconomists explaining cross-country differences in
income (Sachs and Warner, 1997).

7. Conclusions
This paper shows that the negative effect of high temperatures goes beyond contemporaneous
effects on mortality. Using three large and nationally representative surveys in Mexico,
combined with district-level temperature information, I show that exposure to high temperatures
early in life has long-lasting consequences. In particular, higher temperatures during infancy (1-4
years of age) and adolescence (10-14) have significant negative effects on adult height.
The methodology used in this paper is validated by the use of falsification tests where no
effects are found on weight, a short-term of health, and the likelihood of having accident, an
unrelated measure of health. While there are not significant differences by gender, the paper
shows that the negative effects are stronger for individuals living in poorer districts. Thus, the
effects of high temperature would lead to an overall decline in health that is going to amplify
11

health differences by socio-economic status. These differences are critical to the design of
effective policies to buffer the negative effect on health produced by climate change.

Figure 1. Number of Days in Temperature Bins from Historic and Projected
Data in Mexico

Source: http://cesm.ucar.edu/models/ccsm3.0/

15

Figure 2. Geographical Distribution of Weather Stations in Mexico

Note: Each red dot represents a weather station in Mexico. Source: Authorâ&#x20AC;&#x2122;s calculation based on
GIS data from http://www.conabio.gob.mx/informacion/gis/

16

Figure 3. Within-Year Variation in Extreme Temperatures

Note: The numbers shown are the residuals of regressing the proportion of days in a
year above 80Ë&#x161;F per weather station against station fixed-effects. The black solid line
refers to the mean across all stations within a year. The blue dashed lines are the 25 and
75 percentiles, the red broken lines are the 5 and 95 percentiles. The hollow circles
capture the 1 and 99 percentiles.

17

Figure 4. Adult Height (cm.) and Average Daily Temperature
at Each Stage of the Life Cycle

Note: In each graph, the solid blue line represents the point estimate and the dashed line the
95% confidence intervals. Each estimate shows the effect on adult health outcomes of
having more days with temperature above 50Ë&#x161;F. The stages of the life cycle were
aggregated into four groups: Pre, which represents in utero exposure (i.e., the year before
birth), Inf (aged between 1 and 4), Child (5-9) and Teen (10-15.) All regressions control for
fixed effects at the district, birth cohort and survey year as well as state trends and
individual characteristics (i.e., education, marital status and gender.)

18

Figure 5. Adult Weight (Kg.) and Average Daily Temperature
at Each Stage of the Life cycle

Note: In each graph, the solid blue line represents the point estimate and the dashed line the
95% confidence intervals. Each estimate shows the effect on adult health outcomes of
having more days with temperature above 50Ë&#x161;F. The stages of the life cycle were
aggregated into four groups: Pre, which represents in utero exposure (i.e., the year before
birth), Inf (aged between 1 and 4), Child (5-9) and Teen (10-15.) All regressions control for
fixed effects at the district, birth cohort and survey year as well as state trends and
individual characteristics (i.e., education, marital status and gender.)

19

Figure 6. Likelihood of Recent Accidents and Average Daily Temperature
at Each Stage of the Life Cycle

Note: In each graph, the solid blue line represents the point estimate and the dashed line the
95% confidence intervals. Each estimate shows the effect on adult health outcomes of
having more days with temperature above 50Ë&#x161;F. The stages of the life cycle were
aggregated into four groups: Pre, which represents in utero exposure (i.e., the year before
birth), Inf (aged between 1 and 4), Child (5-9) and Teen (10-15.) All regressions control for
fixed effects at the district, birth cohort and survey year as well as state trends and
individual characteristics (i.e., education, marital status and gender.)

20

Figure 7. Heterogeneous Effects on Height by Gender (in cm.)

Note: In each graph, the solid blue line represents the point estimate and the dashed line the
95% confidence intervals. Each estimate shows the effect on adult health outcomes of
having more days with temperature above 50Ë&#x161;F. The stages of the life cycle were
aggregated into four groups: Pre, which represents in utero exposure (i.e., the year before
birth), Inf (aged between 1 and 4), Child (5-9) and Teen (10-15.) All regressions control for
fixed effects at the district, birth cohort and survey year as well as state trends and
individual characteristics (i.e., education, marital status and gender.)

Note: In each graph, the solid blue line represents the point estimate and the dashed line the
95% confidence intervals. Each estimate shows the effect on adult health outcomes of
having more days with temperature above 50Ë&#x161;F. The stages of the life cycle were
aggregated into four groups: Pre, which represents in utero exposure (i.e., the year before
birth), Inf (aged between 1 and 4), Child (5-9) and Teen (10-15.) All regressions control for
fixed effects at the district, birth cohort and survey year as well as state trends and
individual characteristics (i.e., education, marital status and gender.)

Note: * significant at 10%; ** significant at 5%; *** significant at 1%. Robust standard errors clustered at the
district level. Controls include district fixed-effects.

23

Figure A1. Self-Reported Overall Health Status (=1) and Average Daily Temperature
at Each Stage of the Life cycle

Note: In each graph, the solid blue line represents the point estimate and the dashed line the 95%
confidence intervals. Each estimate shows the effect on adult health outcomes of having more
days with temperature above 50Ë&#x161;F. The stages of the life cycle were aggregated into four groups:
Pre, which represents in utero exposure (i.e., the year before birth), Inf (aged between 1 and 4),
Child (5-9) and Teen (10-15.) All regressions control for fixed effects at the district, birth cohort
and survey year as well as state trends and individual characteristics (i.e., education, marital status
and gender.)

long-term effect of climate change on health: evidence from heat waves in mexico

this paper uses year-to-year variation in temperature to estimate the long-term effects of climate change on health outcomes in mexico. combining temperature data at the district level and three rounds of nationally representative household surveys, an individual's health as an adult is matched with the history of heat waves from birth to adulthood. a flexible econometric model is used to identify critical health periods with respect to temperature. it is shown that exposure to higher temperatures early in life has negative consequences on adult height. most importantly, the effects are concentrated at the times where children experience growth spurts: infancy and adolescence. the robustness of these findings is confirmed when using health outcomes derived from accidents, which are uncorrelated with early exposure to high temperatures.